Author(s)
Rishabh Mishra
- Manuscript ID: 140895
- Volume: 2
- Issue: 7
- Pages: 697–701
Subject Area: Other
Abstract
The rapid growth of Artificial Intelligence (AI) has changed the generation, preservation, and sharing of digital data, presenting both new prospects and legal challenges. One of the most notable innovations in this regard is the advent of deepfake technologies which involve synthetic audio, video, and images created by means of AI that can accurately mimic real people and situations. However, apart from having many beneficial applications in such spheres as education, entertainment, and innovation, deepfakes represent a significant threat to the reliability of digital evidence which is vital to modern dispute resolution processes.
Deepfake technologies have become increasingly worrying to many. For example, the Global Risks Report 2025 published by the World Economic Forum lists AI-generated misinformation and disinformation among the top global risks of a shorter term while UNESCO's Guidelines for the Use of AI Systems in Courts and Tribunals (2024) stress the importance of transparency, human oversight, and necessary precautions to maintain the credibility of judicial and quasi-judicial proceedings. Moreover, in India, the Ministry of Electronics and Information Technology (MeitY) has provided relevant guidelines concerning the role of digital intermediaries in preventing the spread of deceptive AI-generated content.
The aforementioned issues acquire importance especially in cases of arbitration, as the process more and more depends upon electronic agreements, e-mails, digital communications, and virtual meetings. While the adaptability of the arbitral procedure remains its most valuable asset, it also subjects arbitrators to the threat of advanced AI-generated evidence which might be hard to spot by traditional means. With technology developing much faster than law, the question facing arbitration is whether it will be possible for the current standard of proof to reliably separate real digital evidence from AI-based deception.